After Trial MOOC, A Visual Interface For Moodle Analytics Begins To Take Shape

The Learning Analytics team at Moodle HQ is ready to help with all of your questions about how to make a better learning experience. All it asks in return is that you embrace the fact that data is part of the answer.

A series of ongoing efforts by the team, lead by Elizabeth Dalton, is showing progress, and the results are likely to be available to Moodlers soon. The recently held “Alpha MOOC,” whose outcomes might help define aspects of the visual learning analytics interface, arguably the team’s boldest move yet, and the moodle.org/analytics page containing all publicly available information are the main frontlines. For advanced Moodlers, the “Epic” Issue in the Moodle Tracker (MDL-62166) is the best way to track the many threads of progress, as it shows development not only for the visual layout, but the components needed to build a robust and responsive interface. The current challenge is not small: Make users able, without a line of code, to create their own model with variables, predictions, and testing options. Of course, making the most of a predictive model benefits from a deeper understanding of the science and practice at work.

Moodle Learning Analytics MOOC, Alpha

More than 60 users, from enthusiasts to developers, took part in an “Alpha” or preliminary session of the MOOC, which should roll out later on for the public. It covers five components of the analytics model, plus an overview and a broader discussion about how learning analytics fit within the definition and principles of “quality education.” Students could choose one of two tracks: “Developer” or “Researcher,” each with specific assignments. The summaries of the MOOC, listed below, give us an idea of the challenges and upcoming developments for Moodle Analytics:

Overview. Shares some preliminary definitions, including the types of analytics available (Descriptive, Diagnostic, Predictive, and Prescriptive) and how different curriculum schools of thought (Academic Scholar, Social Efficiency, Learner-Centered, and Social Reconstruction) would take advantage of them.

Targets. The first element of an analytics model, targets define the item that will be measured, predicted, and possibly altered by a deliberate learning intervention.

Indicators. The predictors of the target outcome. In general, one or more indicators help predict targets. The model assigns weights to the indicators based on how well they have predicted the target in the past. It is possible that there are targets for which new indicators are necessary, which can be created through custom Moodle data classes or by using external data sources.

Time-Splitting. Going deeper into technical details, this section discusses lengths, sizes, and frequencies of data collected, processed, and produced in a model. There are often compromises required for a functional model, many of them depending on the specific context of the learning organization, even though some indicators have clear time-splitting methods for most cases, as well as performance limitations. Moodle’s models support a vast array of combinations.

Insights, Notifications, and Actions. This section covers practical considerations based on new Analytics insight: How should we learn or be notified of new information? What should Moodle enable users to change accordingly, and how much of it can be done automatically? And how can we evaluate and refine the model? The way Analytics informs the design and implementation of a learning intervention is perhaps the most exciting part, but it is also quite complex. Decisions about when it is best to notify users and the “sentiment” involved (positive, negative, neutral) require knowledge from several fields, not the least of them social psychology, behavioral economics, ethics, maybe even storytelling.

A broader debate on “Quality Education.” It circles back to the defining elements, definitions, and goals of analytics practice, as required by the school’s curriculum. It also expands on issues surrounding analytics: security, privacy, ethics, and quality of research.

The course materials are expected to be available to the public by the end of July.

Moodle Learning Analytics Most Valuable Resource: You

More than any keen insight about the power of Moodle Analytics and LMS Data, the most important takeaway Dalton would like Moodlers go with is the importance of participation. And not only from developers or researchers. Moodle HQ’s development agenda can prioritize the visual interface if the community’s voices make themselves be heard.

On the data front, a prediction is only as good as the quality and quantity of the data on which its built. Likewise, the future development of tools in Moodle is a matter of how active the interest in Analytics continues to be. Developers are welcome to share their models or other developments here, and Moodle admins can help the engine’s learning by sharing anonymized student data.